Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Police Body Worn Cameras and Privacy: Retaining Benefits While Reducing Public Concerns

Recent high-profile incidents of police misconduct have led to calls for increased police accountability. One proposed reform is to equip police officers with body worn cameras, which provide more reliable evidence than eyewitness accounts. However, such cameras may pose privacy concerns for individuals who are recorded, as the footage may fall under open records statutes that would require the footage to be released upon request. Furthermore, storage of video data is costly, and redaction of video for release is time-consuming. While exempting all body camera video from release would take care of privacy issues, it would also prevent the public from using body camera footage to uncover misconduct. Agencies and lawmakers can address privacy problems successfully by using data management techniques to identify and preserve critical video evidence, and allowing non-critical video to be deleted under data-retention policies. Furthermore, software redaction may be used to produce releasable video that does not threaten the privacy of recorded individuals

The effect of feedback at test on source memory performance

Previous research has demonstrated that witnesses can come to believe they saw details that were only suggested to them after the witnessed event. For both theoretical and practical reasons, there is interest in developing techniques that reduce the effect of misleading post-event information. The present study examined the effect of receiving feedback at the time of retrieval on eyewitness suggestibility. All participants watched a videotaped crime of a home burglary and then answered questions that contained misleading information. On a final source memory test, participants that were provided with feedback as to the accuracy of their attributions during the first part of the test, significantly reduced the number of source misattributions made on the second part of the test. Thus, feedback at retrieval appears to be a promising technique for reducing eyewitness memory errors

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

Disruption of Individual Mobility Ahead? A Longitudinal Study of Risk and Benefit Perceptions of Self-Driving Cars on Twitter

In this paper, we address the question if there is a disruption of individual mobility by self-driving cars ahead. In order to answer this question, we take the user perspective and conduct a longitudinal study of social media data about self-driving cars from Twitter. The study analyzes 601,778 tweets from March 2015 to July 2016. We use supervised machine learning classification to extract relevant information from this huge amount of unstructured text. Based on the classification, we analyze how risk and benefit perceptions of self-driving cars develop over time, and how they are influenced by certain events. Based on the perceived risks and benefits, we draw conclusions for the acceptance of self-driving cars. Our study shows that a disruptive innovation of self-driving cars is not likely as risk and benefit perception issues indicate a lack of acceptance. We provide suggestions for improving the acceptance of self-driving cars